References
- Mayeux R. Biomarkers: potential uses and limitations. NeuroRx. 1(2): 182–188.
- Strimbu K, Tavel JA. What are biomarkers?. Curr Opin HIV AIDS. 2010;5(6):463–466.
- Craig-Schapiro R, Fagan AM, Holtzman DM. Biomarkers of alzheimer's disease. Neurobiol Dis. 2009;35(2):128–140.
- Zheng Y, Cai T, Jin Y, et al. Evaluting prognostic accuracy of biomarkers under competing risk. Biometrics. 2012;68(2):388–396.
- Chen W, Samuelson FW, Gallas BD, et al. On the assessment of the added value of new predictive biomarkers. BMC Med Res Methodol. 2013;13:98.
- Moons KGM, de Groot JAH, Linnet K, et al. Quantifying the added value of a diagnostic test or marker. Clin Chem. 2012;58(10):1408–1417.
- Linnet K, Bossuyt PMM, Moons KGM, et al. Quantifying the accuracy of a diagnostic test or marker. Clin Chem. 2012;58(9):1292–1301.
- Ridker PM, Buring JE, Rifai N, et al. Development and validation of improved algorithms for the assessment of global cardiovascular risk in women. J Am Med Assoc. 2007;297:611–619.
- Ridker PM, Paynter NP, Rifai N, et al. C-reactive protein and parental history improve global cardiovascular risk prediction: the reynolds risk score for men. Circulation. 2008;118:2243–2251.
- Wilson PWF, Pencina M, Jacques P, et al. C-Reactive protein and reclassification of cardiovascular risk in the framingham heart study. Circ Cardiovasc Qual Outcomes. 2008;1(2):92–97.
- Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 1982;143:29–36.
- Birgit G. Analysis of biomarker data: logs, odds ratios, and receiver operating characteristic curves. Currunet Opinion in HIV and AIDS. 2010;5(6):473–9.
- Pepe MS. An interpretation for the roc curve and inference using glm procedures. Biometrics. 2000;56:352–359.
- Janes H, Pepe MS. Adjusting for covariates in studies of diagnostic, screening, or prognostic markers: an old concept in a new setting. Am J Epidemiol. 2008;168(1):89–97.
- Janes H, Pepe MS. Adjusting for covariate effects on classification accuracy using the covariate-adjusted receiver operating characteristic curve. Biometrika. 2009;96(2):371–382.
- Janes H, Pepe MS. Adjusting for covariate effects on classification accuracy using the covariate-adjusted ROC curve. UW Biostat Work Pap Ser. 2009;96(2):371–382.
- Rodríguez-Alvarez MX, Roca-Pardiñas J, Cadarso-Suárez C. Roc curve and covariates: extending induced methodology to the non-parametric framework. Stat Comput. 2011;21(4):483–499.
- Harrell Jr FE, Califf RM, Pryor DB, et al. Evaluating the yield of medical tests. J Am Med Assoc. 1982;247:2543–46.
- Pepe MS. Three approaches to regression analysis of receiver operating characteristic curves for continuous test results. Biometrics. 1998;54(1):124–135.
- Faraggi D. Adjusting receiver operating characteristic curves and related indices for covariates. J R Stat Soc Ser D Stat. 2003;52(2):179–192.
- Cai T, Pepe MS. Semiparametric receiver operating characteristic analysis to evaluate biomarkers for disease. J Am Stat Assoc. 2002;97(460):1099–1107.
- Alonzo TA, Pepe MS. Distribution-free roc analysis using binary regression techniques. Biostatistics. 2002;3(3):421–432.
- Austin PC. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behav Res. 2011;46(3):399–424.
- D'Agostino RB. Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group. Stat Med. 1998;17:2265–2281.
- Weitzen S, Lapane KL, Toledano AY, et al. Principles for modeling propensity scores in medical research: a systematic literature review. Pharmacoepidemiol Drug Saf. 2004;13:841–853.
- Han S, Andrei AC, Tsui KW, et al. Roc analysis using covariate balancing propensity scores with an application to biochemical predictors for thyroid cancer. Communications in Statistics Part B: Simulation and Computation. 2022;51(1):374–390.
- Galadima HI, McClish DK. Controlling for confounding via propensity score methods can result in biased estimation of the conditional auc: A simulation study. Pharm Stat. 2019;18(5):568–582.
- McCaffrey DF, Griffin BA, Almirall D, et al. A tutorial on propensity score estimation for mutiple treatments using generalized boosted model. Stat Med. 2013;30–32(19):3388–3414.
- Coocicnough DJ, Rossmanrr K, Lusted LB. Radiographic applications of receiver operating characteritic (roc) curves. Radiology. 2003;229(1):3–8.
- Goncalves L, Subtil A, Oliveira MR. Roc curve estimation: an overview. Revstat Stat J. 2014;12(1):1–20.
- Metz CE. Basic principles of roc analysis. Semin Nucl Med. 1978;8(4):283–298.
- Faraggi D, Reiser B. Estimation of the area under the roc curve. Stat Med. 2002;30(20):3093–3106.
- Metz CE. Basic principles of ROC analysis. Semin Nucl Med. 1978;8:283–298.
- González-Manteiga W, Pardo-Fernández JC, Keilegom IV. Roc curves in non-parametric location-scale regression models. Scand J Stat. 2011;38:169–184.
- Fan J, Marron JS. Fast implementations of nonparametric curve estimators. J Comput Graph Stat. 1994;3(1):35–56.
- Müller H-G, Schmitt T. Kernel and probit estimates in quantal bioassay. J Am Stat Assoc. 1988;83(403):750–759.10.1080/01621459.1988.10478658
- Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika. 1983 Apr;70(1):41–55.10.1093/biomet/70.1.41
- Olmos A, Govindasamy P. A practical guide for using propensity score weighting in R. 2015;20(13).
- Zhu Y, Coffman DL, Ghosh D. A boosting algorithm for estimating generalized propensity score with continuous treatment. J Causal Inference. 2015;3(1):25–40.
- Austin PC. Assessing covariate balance when using the generalized propensity score with quantitative or continuous exposures. Stat Methods Med Res. 2019;28(5):1365–1377. PMID: 29415624.
- Ghosh D. Propensity score modelling in observational studies using dimension reduction methods. Stat Probab Lett. 2011;81(7):813–820. ISSN 0167-7152. Statistics in Biological and Medical Sciences.
- Cook RD, Lee H. Dimension reduction in binary response regression. J Am Stat Assoc. 1999;94(448):1187–1200.
- Li KC. Sliced inverse regression for dimension reduction. J Am Stat Assoc. 1991;86(414):316–327.
- Dawid A. Conditional independence in statistical theory. J R Stat Soc Series B Stat Methodol. 1979;41(1):1–31.ISSN 00359246.
- Akter T, Sarker EB, Rahman MS. A tutorial on gee with applications to diabetes and hypertension data from a complex survey. J Biomed Anal. 2018;1(1):37–50.
- Roy PK, Khan MHR, Akter T, et al. Exploring socio-demographic-and geographical-variations in prevalence of diabetes and hypertension in bangladesh: Bayesian spatial analysis of national health survey data. Spat Spatiotemporal Epidemiol. 2019;29:71–83.
- NIPORT, Mitra-Associates, and Macro International. Bangladesh Demographic and Health Survey 2011. Technical report, National Institute of Population Research and Training (NIPORT); Dhaka, Bangladesh, and Calverton, Maryland, USA, 2011.
- Dua S, Bhuker M, Sharma P, et al. Body mass index relates to blood pressure among adults. North Am J Med Sci. 2014;6(2):89–95.